Paper Info
Title
Mining and ranking of generalized multi-dimensional frequent subgraphs
Abstract
Frequent pattern mining is an important research field and can be applied to different labeled data structures ranging from itemsets to graphs. There are scenarios where a label can be assigned to a taxonomy and generalized patterns can be mined by replacing labels by their ancestors. In this work, we propose a novel approach to generalized frequent subgraph mining. In contrast to existing work, our approach considers new requirements from use cases beyond molecular databases. In particular, we support directed multigraphs as well as multiple taxonomies to deal with the different semantic meaning of vertices. Since results of generalized frequent subgraph mining can be very large, we use a fast analytical method of p-value estimation to rank results by significance. We propose two extensions of the popular gSpan algorithm that mine frequent subgraphs across all taxonomy levels. We compare both algorithms in an experimental evaluation based on a database of business process executions represented by graphs.
Year
DOI
Venue
2017
10.1109/ICDIM.2017.8244685
2017 Twelfth International Conference on Digital Information Management (ICDIM)
Keywords
Field
DocType
generalized multidimensional frequent subgraphs,frequent pattern mining,generalized patterns,generalized frequent subgraph mining,research field,labeled data structures,gSpan algorithm,taxonomy levels
Data mining,Data modeling,Data structure,Multi dimensional,Use case,Business process,Vertex (geometry),Ranking,Computer science,Labeled data
Conference
ISBN
Citations 
PageRank 
978-1-5386-0665-0
1
0.35
References 
Authors
30
5
Name
Order
Citations
PageRank
André Petermann1516.17
Giovanni Micale261.80
Giacomo Bergami322.05
Alfredo Pulvirenti438823.92
Erhard Rahm57415655.09